TransUnion found strong marketer confidence in artificial intelligence, but weaker readiness to measure its impact, with 89% expecting AI-enabled marketing investment to grow over the next 12 to 24 months.
The study surveyed 100 senior marketing and technology leaders at major U.S. brands. It found 64% were confident they would meet their AI goals, while only 42% rated their organization’s people readiness as high and 36% said the same for data and process readiness.
Less than half of respondents, 48%, said they had enough visibility into platform-level AI to make optimization decisions with confidence. TransUnion described the results as a “confidence-readiness paradox.”
Matt Spiegel, executive vice president of TruAudience Growth Strategy at TransUnion, said data was the biggest gap. “AI can compensate for a lot of things, but it can’t compensate for incomplete or disconnected data. If you’re feeding AI incomplete information, you’re going to get outcomes that are less predictive than you hoped.”
Spiegel said problems with people and processes stem from weak data foundations. He said customer records, transaction history and behavioral signals need to be connected before governance and skills can improve.
The study also found broad limits in measurement across digital channels. Sixty-nine percent of respondents said walled garden blind spots restricted their ability to evaluate AI’s effectiveness, while 70% said cross-channel blind spots made it difficult to track AI’s effect across the customer journey.
Spiegel said the issue was not confined to one platform because marketers were working across paid social, retail media, connected TV, search and owned channels without a single view of the customer journey.
He said AI search should be treated as part of the same measurement problem. “AI search is another example of why independent measurement is becoming more important,” Spiegel said.
He said consumers are increasingly discovering brands through AI-generated answers instead of traditional search results, reducing marketers’ visibility into how recommendations are created and what influenced them. He described that as an extension of the walled garden problem rather than a separate issue.
Spiegel said the main mistake leaders make is focusing on AI strategy before defining the business problem they want to solve. “The biggest mistake he sees isn’t a bad AI strategy. It’s leaders asking ‘what’s our AI strategy’ instead of ‘what business problem are we solving.’”
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